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Laboratoire/Entreprise : LIFO (Laboratoire d’Informatique Fondamentale d’
Durée : 3 ans
Contact : guillaume.cleuziou@univ-orleans.fr
Date limite de publication : 2022-07-15

Contexte :
Improving the pedagogical efficiency of programming training platforms is a fast-growing problem that requires the construction of fine-grained and exploitable representations of learners’ programs. In this PhD thesis, we are interested in learning representations (or embeddings) of programs for pedagogical purposes.

Two main strategies for learning program embeddings have been proposed so far: approaches based on the observation of program execution results (Wang et al., 2018) and those based on the syntactic analysis of programs (Alon, 2019). In this thesis, we will consider an original approach at the intersection of these two strategies based on a representation of programs via an abstract execution sequence and thus aiming to jointly take advantage of both functional and syntactic descriptions of programs (Cleuziou&Flouvat, 2021).

Sujet :
In order to carry out this work, it will be necessary to draw inspiration from models developed for text mining purpose and to study their adaptability for computer programs. Given the specificities of this type of data (restricted vocabulary, importance of ‘words’ order, etc.), it will be interesting to consider either simple (e.g. word2vec), recurrent (e.g. LSTM, GRU), convolutive or Transformer-like (e.g. BERT) neural models.

The fundamental part of the thesis will be backed up by applicative concerns on educational data, aiming at the development of ‘Augmented Pedagogy’ environments for teachers. The aim will be to identify support tasks on which the teacher could be assisted (e.g. detection of learner ‘drop-outs’, suggestion of feedbacks, etc.) and to implement them in a Research & Development process integrated with the digital tools used by the institution’s training courses.

Profil du candidat :
Proficiency (speaking and writing) in French or in English.

Strong skills in programming languages such as Java and Python.

Experience in machine learning, data mining and deep learning.

Interest in Educational data analysis is appreciated.

Formation et compétences requises :
Master’s degree and/or engineering school degree in Mathematics/Computer Science.

Adresse d’emploi :
LIFO (Laboratoire d’Informatique Fondamentale d’Orléans)
Université d’Orléans
France

Document attaché : 202205301010_IA4CSE_PhD_Proposal_2022.pdf